Poor visibility degrades the perceptual image quality as well as the performance of the computer vision algorithms such as surveillance, object detection, tracking and segmentation. Poor visibility in bad weather such as fog, mist and haze caused by the water droplets present in the air. These droplets are very small (1-10 μm)[K. Garg and S. K. Nayar, \Vision and Rain”, International Journal of Computer Vision, Vol. 75, No. 1, pp. 3-27, 2007.] and steadily float in the air. Due to the presence of fog, mist and haze light scattered in the atmosphere before it reaches the camera. Here onwards the word fog will be used for all fog, mist, and haze. Two fundamental scattering phenomena which cause the scattering are attenuation and airlight. A light beam travels from a scene point through the atmosphere, it gets attenuated due to the scattering by the atmospheric particles, this phenomena is called attenuation which reduces the contrast in the scene. Light coming from the source is scattered towards the camera and leads to the shift in color. This phenomena is called airlight. Airlight increases with the distance from the object. It is noted that the fog effect is the function of the distance between the camera and the object. Hence removal of fog requires the estimation of the depth map or the airlight map. If input is only a single foggy image then estimation of the depth map is under constrained. Generally estimation of depth requires two images. Therefore many methods have been proposed which use multiple images. Schechner et al [Y. Y. Schechner, S. G. Narasimhan, and S. K. Nayar, \Instant dehazing of images using polarization”, IEEE Computer Society Conference on Computer Vision and Pattern Recognition, pp. 325-332, 2001.] proposed a method based on polarization. This method removes the fog through two or more images taken with different degrees of polarization. But this method can not be applied on existing databases. In past few years many algorithms have been proposed for the removal of fog which use single image.
Fattal [R. Fattal, \Single image dehazing”, International Conference on Computer Graphics and Interactive Techniques archive ACM SIGGRAPH, pp. 1-9, 2008.] proposed a method which is based on the independent component analysis (ICA). This method estimates the optical transmission in hazy scenes. Based on this estimation, the scattered light is eliminated to increase scene visibility and recover haze from scene contrasts. Here restoration is based on the color information, hence this method can not be applied for the gray image. This method fails when there is dense fog because dense fog is often colorless.
Tan [R. T. Tan, \Visibility in bad weather from a single image”, IEEE conference on Computer Vision and Pattern Recognition, pp. 1-8, 2008.] proposed a method based on spatial regularization from a single color or gray scale image. Tan removed the fog by maximizing the local contrast of the image but restored image looks over saturated.
Kopf et al [J. Kopf, B. Neubert, B. Chen, M. Cohen, D. Cohen-Or, O. Deussen, M. Uyttendaele, and D. Lischinski, \Deep photo: Model-based photograph enhancement and viewing”, ACM Transactions on Graphics, Vol. 27, No. 5, pp. 116:1-116:10, 2008.] proposed a method based on the use of a 3D model of the scene. This method is application dependent and requires the interactions with an expert.
He et al [K. He, J. Sun, and X. Tang, \Single image haze removal using dark channel prior”, IEEE International Conference on Computer Vision and Pattern Recognition, pp. 1956-1963, 2009.] proposed a method based on the matting and dark channel prior from a single color or gray scale image. But when the scene objects are bright similar to the atmospheric light, underlying assumptions of this algorithm do not remain valid. Tarel et al [J. P. Tarel and N. Hautiere, \Fast visibility restoration from a single color or gray level image”, IEEE International Conference on Computer Vision, pp. 2201-2208, 2009.] proposed a fast visibility restoration algorithm. This method assumes the airlight as a percentage between the local standard deviation and the local mean of the whiteness. This method based on linear operations but requires many parameters for the adjustment for optimal result.
It is thus evident that there exist systems to remove fog from images captured by multiple cameras viz. stereoscopic imaging and there are also systems that do the same job using only one camera. However, no existing system attempted to remove fog from videos. The present invention involves a system and method using single camera approach for removing fog from images as well as videos which save cost and computation.